Datasets:

nlpai-lab
/

ko_commongen_v2

	---
	dataset_info:
	features:
	- name: concept_set
	dtype: string
	- name: '1'
	dtype: string
	- name: '2'
	dtype: string
	- name: '3'
	dtype: string
	- name: '4'
	dtype: string
	- name: gold
	dtype: int64
	splits:
	- name: train
	num_bytes: 1658
	num_examples: 5
	- name: test
	num_bytes: 267519
	num_examples: 847
	download_size: 182862
	dataset_size: 269177
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: test
	path: data/test-*
	task_categories:
	- text-generation
	language:
	- ko
	pretty_name: KoCommonGEN-V2
	---

	# 🌠 KoCommonGEN v2

	KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models (ACL 2024-Findings)

	Jaehyung Seo, Jaewook Lee, Chanjun Park, SeongTae Hong, Seungjun Lee and Heuiseok Lim

	🏫 [NLP & AI Lab](https://blpkorea.cafe24.com/wp/), Korea University

	---
	### 🔥 News
	- September 27, 2023: Provided data support for the [Open Ko-LLM Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
	- August 7, 2024: Dataset Release
	- August 10, 2024: Experimental Results for the New Models Added
	- August 14, 2024: Presented a research paper at ACL 2024





	### 👥 Human Evaluation

	We recruited 22 native Korean speaking volunteers as human evaluators and paid them $0.8 per question.

	\| Model \| # \| Average Score \| cohen's kappa \| Krippendorff's alpha \|
	\| :-------: \| :--: \| :-----------: \| :-----------: \| :------------------: \|
	\| Human \| 22 \| 0.8395 \| 0.7693 \| 0.7706 \|

	### 🤖 Models (August 10, 2024)

	The results of 2-shot evaluation of the newly released models.

	\| Model \| Size \| Acc_norm \| Stderr \| Link \|
	\| :----------------------------: \| :---: \| :--------: \| :----: \| :----------------------------------------------------------: \|
	\| GPT-4 (June 13, 2023) \| \| 0.7450 \| \| \|
	\| Mistral-Nemo-Instruct \| 12B \| 0.6612 \| 0.0163 \| [🔗](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) \|
	\| Mistral-Nemo-Base \| 12B \| 0.6340 \| 0.0166 \| [🔗](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) \|
	\| Meta-Llama-3.1-8B \| 8B \| 0.6246 \| 0.0166 \| [🔗](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) \|
	\| QWEN2-7B base \| 7B \| 0.6187 \| 0.0167 \| [🔗](https://huggingface.co/Qwen/Qwen2-7B) \|
	\| EXAONE-3.0-7.8B-Instruct \| 7.8B \| 0.6088 \| 0.0168 \| [🔗](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) \|
	\| MLP-KTLim-Bllossom-8B \| 8B \| 0.6057 \| 0.0168 \| [🔗](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) \|
	\| Meta-Llama-3.1-8B-Instruct \| 8B \| 0.6057 \| 0.0168 \| [🔗](meta-llama/Meta-Llama-3.1-8B-Instruct) \|
	\| KULLM3 \| 10.8B \| 0.6033 \| 0.0168 \| [🔗](https://huggingface.co/nlpai-lab/KULLM3) \|
	\| QWEN2-7B inst \| 7B \| 0.5832 \| 0.017 \| [🔗](Qwen/Qwen2-7B-Instruct) \|
	\| Gemma-2-9b-it \| 9B \| 0.5714 \| 0.0170 \| [🔗](https://huggingface.co/google/gemma-2-9b-it) \|
	\| Aya-23-8B \| 8B \| 0.5159 \| 0.0172 \| [🔗](CohereForAI/aya-23-8B) \|
	\| Allganize-Alpha-Instruct \| 8B \| 0.4970 \| 0.0172 \| [🔗](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) \|

	As mentioned in the paper, it is possible to evaluate various models.



	### 🇰🇷🇺🇸🇯🇵🇨🇳🇪🇸 Code-switching

	The dataset can be found on Hugging Face at: [nlpai-lab/ko_commongen_v2_code_switching](https://huggingface.co/datasets/nlpai-lab/ko_commongen_v2_code_switching)

	This dataset contains code-switching data for the following languages:
	- Korean (korean)
	- English (english)
	- Japanese (japan)
	- Chinese (china)
	- Spanish (espanol)

	(The code-switching data relies on machine translation, which may result in some inaccuracies.)

	### 📖 Citation

	```
	@inproceedings{seo2024Kocommongenv2,
	title = "KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models",
	author = "Jaehyung Seo and Jaewook Lee and Chanjun Park and SeongTae Hong and Seungjun Lee and Heuiseok Lim",
	booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
	month = August,
	year = "2024",
	address = "Bangkok, Thailand",
	publisher = "Association for Computational Linguistics",
	url = "TBD",
	doi = "TBD",
	pages = "TBD"}
	```

	### 🚨 Warning!

	This dataset contains some instances of toxic speech.


	### 🙏 Acknowledgement

	We sincerely appreciate the dedication of Chanjun Park, Sanghoon Kim and Sunghun Kim (Sung Kim) from Upstage AI in managing one of the benchmark datasets for the
	[Open Ko-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).